

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase			:				List[Any]   =     logging.get_logger(__name__)

enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	UNetaDModel
							UpperCAmelCase_						:		Dict								=	'''sample'''




							@property
							def       snake_case			(					self						)		->       Optional[int]:
														A      :					List[Any]      		=					4
														A      :					List[Any]      		=					3
														A      :					Any      		=					(32, 32)

														A      :					List[Any]      		=					floats_tensor((batch_size, num_channels) + sizes						).to(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					torch.tensor([10]						).to(__UpperCAmelCase						)

														return {"sample": noise, "timestep": time_step}




							@property
							def       snake_case			(					self						)		->       Dict:
														return (3, 32, 32)




							@property
							def       snake_case			(					self						)		->       Tuple:
														return (3, 32, 32)






							def       snake_case			(					self						)		->       Dict:
														A      :					Tuple      		=					{
														    '''block_out_channels''': (32, 64),
														    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
														    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
														    '''attention_head_dim''': 3,
														    '''out_channels''': 3,
														    '''in_channels''': 3,
														    '''layers_per_block''': 2,
														    '''sample_size''': 32,
														}
														A      :					Union[str, Any]      		=					self.dummy_input
														return init_dict, inputs_dict







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[int]								=	UNetaDModel
							UpperCAmelCase_						:		Optional[int]								=	'''sample'''




							@property
							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Dict      		=					4
														A      :					Tuple      		=					4
														A      :					List[Any]      		=					(32, 32)

														A      :					Tuple      		=					floats_tensor((batch_size, num_channels) + sizes						).to(__UpperCAmelCase						)
														A      :					List[Any]      		=					torch.tensor([10]						).to(__UpperCAmelCase						)

														return {"sample": noise, "timestep": time_step}




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return (4, 32, 32)




							@property
							def       snake_case			(					self						)		->       List[str]:
														return (4, 32, 32)




							def       snake_case			(					self						)		->       List[Any]:
														A      :					Optional[Any]      		=					{
														    '''sample_size''': 32,
														    '''in_channels''': 4,
														    '''out_channels''': 4,
														    '''layers_per_block''': 2,
														    '''block_out_channels''': (32, 64),
														    '''attention_head_dim''': 32,
														    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
														    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
														}
														A      :					Union[str, Any]      		=					self.dummy_input
														return init_dict, inputs_dict




							def       snake_case			(					self						)		->       Optional[Any]:
														A		,		A      :					str      		=					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''    ,						output_loading_info=__UpperCAmelCase						)

														self.assertIsNotNone(__UpperCAmelCase						)
														self.assertEqual(len(loading_info['''missing_keys''']						)    ,						0						)

														model.to(__UpperCAmelCase						)
														A      :					List[str]      		=					model(**self.dummy_input						).sample

														assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device != '''cuda'''    ,						'''This test is supposed to run on GPU'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														A		,		A      :					Optional[Any]      		=					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''    ,						output_loading_info=__UpperCAmelCase						)
														model.to(__UpperCAmelCase						)
														A      :					str      		=					model(**self.dummy_input						).sample

														assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device != '''cuda'''    ,						'''This test is supposed to run on GPU'''						)
							def       snake_case			(					self						)		->       List[str]:
														# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
														A		,		A      :					Union[str, Any]      		=					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''    ,						output_loading_info=__UpperCAmelCase						)
														model_accelerate.to(__UpperCAmelCase						)
														model_accelerate.eval()

														A      :					str      		=					torch.randn(
														    1    ,						model_accelerate.config.in_channels    ,						model_accelerate.config.sample_size    ,						model_accelerate.config.sample_size    ,						generator=torch.manual_seed(0						)    ,						)
														A      :					Optional[int]      		=					noise.to(__UpperCAmelCase						)
														A      :					int      		=					torch.tensor([10] * noise.shape[0]						).to(__UpperCAmelCase						)

														A      :					int      		=					model_accelerate(__UpperCAmelCase    ,						__UpperCAmelCase						)['''sample''']

														# two models don't need to stay in the device at the same time
														del model_accelerate
														torch.cuda.empty_cache()
														gc.collect()

														A		,		A      :					Optional[Any]      		=					UNetaDModel.from_pretrained(
														    '''fusing/unet-ldm-dummy-update'''    ,						output_loading_info=__UpperCAmelCase    ,						low_cpu_mem_usage=__UpperCAmelCase						)
														model_normal_load.to(__UpperCAmelCase						)
														model_normal_load.eval()
														A      :					int      		=					model_normal_load(__UpperCAmelCase    ,						__UpperCAmelCase						)['''sample''']

														assert torch_all_close(__UpperCAmelCase    ,						__UpperCAmelCase    ,						rtol=1E-3						)






							def       snake_case			(					self						)		->       str:
														A      :					Optional[int]      		=					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''						)
														model.eval()
														model.to(__UpperCAmelCase						)

														A      :					int      		=					torch.randn(
														    1    ,						model.config.in_channels    ,						model.config.sample_size    ,						model.config.sample_size    ,						generator=torch.manual_seed(0						)    ,						)
														A      :					Dict      		=					noise.to(__UpperCAmelCase						)
														A      :					List[Any]      		=					torch.tensor([10] * noise.shape[0]						).to(__UpperCAmelCase						)

														with torch.no_grad():
																					A      :					Optional[Any]      		=					model(__UpperCAmelCase    ,						__UpperCAmelCase						).sample

														A      :					Union[str, Any]      		=					output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														A      :					Dict      		=					torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
														# fmt: on

														self.assertTrue(torch_all_close(__UpperCAmelCase    ,						__UpperCAmelCase    ,						rtol=1E-3						)						)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	UNetaDModel
							UpperCAmelCase_						:		Union[str, Any]								=	'''sample'''




							@property
							def       snake_case			(					self    ,						__UpperCAmelCase=(32, 32)						)		->       Optional[Any]:
														A      :					int      		=					4
														A      :					str      		=					3

														A      :					List[Any]      		=					floats_tensor((batch_size, num_channels) + sizes						).to(__UpperCAmelCase						)
														A      :					int      		=					torch.tensor(batch_size * [10]						).to(dtype=torch.intaa    ,						device=__UpperCAmelCase						)

														return {"sample": noise, "timestep": time_step}




							@property
							def       snake_case			(					self						)		->       str:
														return (3, 32, 32)




							@property
							def       snake_case			(					self						)		->       Any:
														return (3, 32, 32)




							def       snake_case			(					self						)		->       int:
														A      :					Tuple      		=					{
														    '''block_out_channels''': [32, 64, 64, 64],
														    '''in_channels''': 3,
														    '''layers_per_block''': 1,
														    '''out_channels''': 3,
														    '''time_embedding_type''': '''fourier''',
														    '''norm_eps''': 1E-6,
														    '''mid_block_scale_factor''': math.sqrt(2.0						),
														    '''norm_num_groups''': None,
														    '''down_block_types''': [
														        '''SkipDownBlock2D''',
														        '''AttnSkipDownBlock2D''',
														        '''SkipDownBlock2D''',
														        '''SkipDownBlock2D''',
														    ],
														    '''up_block_types''': [
														        '''SkipUpBlock2D''',
														        '''SkipUpBlock2D''',
														        '''AttnSkipUpBlock2D''',
														        '''SkipUpBlock2D''',
														    ],
														}
														A      :					Optional[int]      		=					self.dummy_input
														return init_dict, inputs_dict




							@slow
							def       snake_case			(					self						)		->       List[str]:
														A		,		A      :					List[str]      		=					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''    ,						output_loading_info=__UpperCAmelCase						)
														self.assertIsNotNone(__UpperCAmelCase						)
														self.assertEqual(len(loading_info['''missing_keys''']						)    ,						0						)

														model.to(__UpperCAmelCase						)
														A      :					List[str]      		=					self.dummy_input
														A      :					int      		=					floats_tensor((4, 3) + (2_56, 2_56)						).to(__UpperCAmelCase						)
														A      :					List[str]      		=					noise
														A      :					Optional[int]      		=					model(**__UpperCAmelCase						)

														assert image is not None, "Make sure output is not None"




							@slow
							def       snake_case			(					self						)		->       str:
														A      :					List[str]      		=					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''						)
														model.to(__UpperCAmelCase						)

														A      :					Dict      		=					4
														A      :					Optional[Any]      		=					3
														A      :					List[str]      		=					(2_56, 2_56)

														A      :					Dict      		=					torch.ones((batch_size, num_channels) + sizes						).to(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					torch.tensor(batch_size * [1E-4]						).to(__UpperCAmelCase						)

														with torch.no_grad():
																					A      :					Optional[Any]      		=					model(__UpperCAmelCase    ,						__UpperCAmelCase						).sample

														A      :					Union[str, Any]      		=					output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														A      :					Dict      		=					torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
														# fmt: on

														self.assertTrue(torch_all_close(__UpperCAmelCase    ,						__UpperCAmelCase    ,						rtol=1E-2						)						)




							def       snake_case			(					self						)		->       List[Any]:
														A      :					Dict      		=					UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''						)
														model.to(__UpperCAmelCase						)

														A      :					Tuple      		=					4
														A      :					str      		=					3
														A      :					Any      		=					(32, 32)

														A      :					List[Any]      		=					torch.ones((batch_size, num_channels) + sizes						).to(__UpperCAmelCase						)
														A      :					int      		=					torch.tensor(batch_size * [1E-4]						).to(__UpperCAmelCase						)

														with torch.no_grad():
																					A      :					str      		=					model(__UpperCAmelCase    ,						__UpperCAmelCase						).sample

														A      :					List[Any]      		=					output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														A      :					List[str]      		=					torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
														# fmt: on

														self.assertTrue(torch_all_close(__UpperCAmelCase    ,						__UpperCAmelCase    ,						rtol=1E-2						)						)






							def       snake_case			(					self						)		->       List[str]:
														# not required for this model
														pass









from ..utils import DummyObject, requires_backends







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														requires_backends(self    ,						['''speech''']						)







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														requires_backends(self    ,						['''speech''']						)






